Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities

Frameworks for logically classifying polynomial-time optimisation problems.

We show that a logical framework, based around a fragment of existential second-order logic formerly proposed by others so as to capture the class of polynomially-bounded P-optimisation problems, cannot hope to do so, under the assumption that P ≠ NP. We do this by exhibiting polynomially-bounded maximisation and minimisation problems that can be expressed in the framework but whose decision versions are NP-complete. We propose an alternative logical framework, based around inflationary fixed-point logic, and show that we can capture the above classes of optimisation problems. We use the inductive depth of an inflationary fixed-point as a means to describe the objective functions of the instances of our optimisation problems

Iterative graph cuts for image segmentation with a nonlinear statistical shape prior

Shape-based regularization has proven to be a useful method for delineating objects within noisy images where one has prior knowledge of the shape of the targeted object. When a collection of possible shapes is available, the specification of a shape prior using kernel density estimation is a natural technique. Unfortunately, energy functionals arising from kernel density estimation are of a form that makes them impossible to directly minimize using efficient optimization algorithms such as graph cuts. Our main contribution is to show how one may recast the energy functional into a form that is minimizable iteratively and efficiently using graph cuts.Comment: Revision submitted to JMIV (02/24/13

Preparing a collection of radiology examinations for distribution and retrieval

OBJECTIVE: Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. MATERIALS AND METHODS: The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals' picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. RESULTS: The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. CONCLUSION: Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/)

Integrated whole transcriptome and DNA methylation analysis identifies gene networks specific to late-onset Alzheimer’s disease

Previous transcriptome studies observed disrupted cellular processes in late-onset Alzheimer\u27s disease (LOAD), yet it is unclear whether these changes are specific to LOAD, or are common to general neurodegeneration. In this study, we address this question by examining transcription in LOAD and comparing it to cognitively normal controls and a cohort of disease controls. Differential transcription was examined using RNA-seq, which allows for the examination of protein coding genes, non-coding RNAs, and splicing. Significant transcription differences specific to LOAD were observed in five genes: C10orf105, DIO2, a lincRNA, RARRES3, and WIF1. These findings were replicated in two independent publicly available microarray data sets. Network analyses, performed on 2,504 genes with moderate transcription differences in LOAD, reveal that these genes aggregate into seven networks. Two networks involved in myelination and innate immune response specifically correlated to LOAD. FRMD4B and ST18, hub genes within the myelination network, were previously implicated in LOAD. Of the five significant genes, WIF1 and RARRES3 are directly implicated in the myelination process; the other three genes are located within the network. LOAD specific changes in DNA methylation were located throughout the genome and substantial changes in methylation were identified within the myelination network. Splicing differences specific to LOAD were observed across the genome and were decreased in all seven networks. DNA methylation had reduced influence on transcription within LOAD in the myelination network when compared to both controls. These results hint at the molecular underpinnings of LOAD and indicate several key processes, genes, and networks specific to the disease

Precise measurement of the top quark mass in the dilepton channel at D0

We measure the top quark mass (mt) in ppbar collisions at a center of mass energy of 1.96 TeV using dilepton ttbar->W+bW-bbar->l+nubl-nubarbbar events, where l denotes an electron, a muon, or a tau that decays leptonically. The data correspond to an integrated luminosity of 5.4 fb-1 collected with the D0 detector at the Fermilab Tevatron Collider. We obtain mt = 174.0 +- 1.8(stat) +- 2.4(syst) GeV, which is in agreement with the current world average mt = 173.3 +- 1.1 GeV. This is currently the most precise measurement of mt in the dilepton channel.Comment: 7 pages, 4 figure